Title :
A Framework for Time Series Forecasts
Author :
Zhang, Dongqing ; Han, Yubing ; Ning, Xuanxi ; Liu, Xueni
Author_Institution :
Coll. of Econ. & Manage., Nanjing Univ. of Aeronaut. & Astronaut., Nanjing
Abstract :
In order to cope with the nonlinear and non-Gaussian time series, a RBF-HMM model, which is based on radial basis function (RBF) neural network with the assumption of measurement noise being hidden Markov model (HMM), is proposed in this paper. On the other hand, most of literatures about neural networks suppose that the number of input is invariable. Obviously, this assumption is improper in some cases. Therefore, sequential Monte Carlo (SMC) method is used for on-line selection of the input order. Firstly, a framework for time series forecasts based on RBF-HMM model is proposed. Secondly, an on-line prediction algorithm based on RBF-HMM model using SMC method is developed. At last, the data of weekly steel price are analyzed and experimental results indicate that the RBF-HMM model is effective.
Keywords :
Monte Carlo methods; hidden Markov models; mathematics computing; radial basis function networks; time series; RBF-HMM; SMC; hidden Markov model; nonGaussian time series forecasts; nonlinear time series forecasts; online prediction algorithm; radial basis function neural network; sequential Monte Carlo method; Autoregressive processes; Computer network management; Feedforward neural networks; Gaussian noise; Hidden Markov models; Multi-layer neural network; Neural networks; Noise measurement; Predictive models; Sliding mode control; Hidden Markov model; Radial basis function neural network; Rao-Blackwellised particle filter; Sequential Monte Carlo; Time series forecasts;
Conference_Titel :
Computing, Communication, Control, and Management, 2008. CCCM '08. ISECS International Colloquium on
Conference_Location :
Guangzhou
Print_ISBN :
978-0-7695-3290-5
DOI :
10.1109/CCCM.2008.316
